Various imaging systems based on traditional approaches exist for assisting the medical professional in identifying the gross region of a target anatomical object, such as ultrasound, computed tomography (CT), magnetic resonance (MR), and fluoroscopic imaging systems. However, anatomical object detection using such systems is not always robust, especially for some challenging detection problems in which the anatomical objects exhibit large variations in anatomy, shape, and/or appearance, as well as noise and artifacts in the medical images. As a result, it is often difficult for a medical professional to quickly and accurately locate the gross region of the target anatomical object when using such imaging systems. For instance, nerve blocks or peripheral nerve blocks (PNBs) are a type of regional anesthesia used for surgical anesthesia as well as for both postoperative and nonsurgical analgesia where it is desired to accurately locate a target anatomical object (e.g., a target nerve). During a PNB, a medical professional injects an anesthetic near a target nerve or bundle of nerves to block sensations of pain from a specific area of the body. However, it can be challenging for a medical professional to quickly and accurately locate the gross region of the target nerve when using currently available imaging systems. For example, for certain nerve block procedures, it is often difficult for a physician to quickly and accurately locate a target nerve bundle via an ultrasound imaging system.
Accordingly, the present disclosure is directed to a system and method for automatic detection, identification, and mapping of anatomical objects from a plurality of real-time images of scenes taken from an anatomical region surrounding a target anatomical object (e.g., a target nerve) in order to provide directions to a user (e.g., medical professional), thus enabling the user to quickly and accurately reach the target anatomical object of interest using deep learning networks that can be implemented via existing imaging systems.